OpenRaiser/Intern-Atlas huggingface dataset:
Airborne Urban Microcells with Grasping End Effectors: A Game Changer for 6G Networks?
Venue: arXiv cs.NI 2021
Year: 2021

Airborne (or flying) base stations (ABSs) embedded on drones or unmanned aerial vehicles (UAVs) can be deemed as a central element of envisioned 6G cellular networks where significant cell densification with mmWave/Terahertz communications will be part of the ecosystem. Nonetheless, one of the key challenges facing the deployment of ABSs is the inherent limited available energy of the drone, which limits the hovering time for serving ground users to the orders of minutes. This impediment deteriorate the performance of the UAV-enabled cellular network and hinders wide adoption and use of the technology. In this paper, we propose robotic airborne base stations (RABSs) with grasping capabilities to increase the serving time of ground users by multiple orders of magnitude compared to nominal hovering based operation. More specifically, to perform the grasping task, the RABS is equipped with a versatile, albeit, general purpose gripper manipulator. Depending on the type of the gripper RABS can provide service in the range of hours, compared to minutes of hovering based ABSs. In theory it is possible that grasping can be energy neutral, hence the time of service can be bounded by the communications energy consumption. To illustrate the case, energy consumption comparison between hovering and grasping is performed in order to reveal the significant benefits of the proposed approach. Finally, overarching challenges, design considerations for RABS, and future avenues of research are outlined to realize the full potential of the proposed robotic aerial base stations.
Da Vinci -- Architecture-Driven Business Solutions
Venue: arXiv cs.SE 2021
Year: 2021

This document has emerged out of Origin's past experiences with architecture-driven application development (AD2), and the need to further formalise and consolidate these experiences.
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised outlier detection, which predicts if a test sample is an outlier or not using only the information from unlabelled inlier data, is an important but challenging task. Recently, methods based on the two-stage framework achieve state-of-the-art performance on this task. The framework leverages self-supervised representation learning algorithms to train a feature extractor on inlier data, and applies a simple outlier detector in the feature space. In this paper, we explore the possibility of avoiding the high cost of training a distinct representation for each outlier detection task, and instead using a single pre-trained network as the universal feature extractor regardless of the source of in-domain data. In particular, we replace the task-specific feature extractor by one network pre-trained on ImageNet with a self-supervised loss. In experiments, we demonstrate competitive or better performance on a variety of outlier detection benchmarks compared with previous two-stage methods, suggesting that learning representations from in-domain data may be unnecessary for outlier detection.
Deciding FO2 Alternation for Automata over Finite and Infinite Words
Venue: arXiv cs.FL 2021
Year: 2021

We consider two-variable first-order logic $\text{FO}^2$ and its quantifier alternation hierarchies over both finite and infinite words. Our main results are forbidden patterns for deterministic automata (finite words) and for Carton-Michel automata (infinite words). In order to give concise patterns, we allow the use of subwords on paths in finite graphs. This concept is formalized as subword-patterns. For certain types of subword-patterns there exists a non-deterministic logspace algorithm to decide their presence or absence in a given automaton. In particular, this leads to $\mathbf{NL}$ algorithms for deciding the levels of the $\text{FO}^2$ quantifier alternation hierarchies. This applies to both full and half levels, each over finite and infinite words. Moreover, we show that these problems are $\mathbf{NL}$-hard and, hence, $\mathbf{NL}$-complete.
Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application
Venue: arXiv cs.IR 2021
Year: 2021

In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications. However, long documents, containing hundreds or even thousands of pages and variable-depth hierarchy, challenge the existing methods. To address these challenges, we develop a framework, namely Hierarchy Extraction from Long Document (HELD), where we "sequentially" insert each physical object at the proper on of the current tree. Determining whether each possible position is proper or not can be formulated as a binary classification problem. To further improve its effectiveness and efficiency, we study the design variants in HELD, including traversal orders of the insertion positions, heading extraction explicitly or implicitly, tolerance to insertion errors in predecessor steps, and so on. The empirical experiments based on thousands of long documents from Chinese, English financial market and English scientific publication show that the HELD model with the "root-to-leaf" traversal order and explicit heading extraction is the best choice to achieve the tradeoff between effectiveness and efficiency with the accuracy of 0.9726, 0.7291 and 0.9578 in Chinese financial, English financial and arXiv datasets, respectively. Finally, we show that logical document hierarchy can be employed to significantly improve the performance of the downstream passage retrieval task. In summary, we conduct a systematic study on this task in terms of methods, evaluations, and applications.
Knowledge-driven Data Ecosystems Towards Data Transparency
Venue: arXiv cs.DB 2021
Year: 2021

A Data Ecosystem offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In this work, we focus on requirements and challenges that data ecosystems face when ensuring data transparency. Requirements are derived from the data and organizational management, as well as from broader legal and ethical considerations. We propose a novel knowledge-driven data ecosystem architecture, providing the pillars for satisfying the analyzed requirements. We illustrate the potential of our proposal in a real-world scenario. Lastly, we discuss and rate the potential of the proposed architecture in the fulfillment of these requirements.
Conelikes and Ranker Comparisons
Venue: arXiv cs.FL 2021
Year: 2021

For every fixed class of regular languages, there is a natural hierarchy of increasingly more general problems: Firstly, the membership problem asks whether a given language belongs to the fixed class of languages. Secondly, the separation problem asks for two given languages whether they can be separated by a language from the fixed class. And thirdly, the covering problem is a generalization of separation problem to more than two given languages. Most instances of such problems were solved by the connection of regular languages and finite monoids. Both the membership problem and the separation problem were also extended to ordered monoids. The computation of pointlikes can be interpreted as the algebraic counterpart of the covering problem. In this paper, we consider the extension the computation of pointlikes to ordered monoids. This leads to the notion of conelikes for the corresponding algebraic framework. We apply this framework to the Trotter-Weil hierarchy and both the full and the half levels of the $\text{FO}^2$ quantifier alternation hierarchy. As a consequence, we solve the covering problem for the resulting subvarieties of $\mathbf{DA}$. An important combinatorial tool are uniform ranker characterizations for all subvarieties under consideration; these characterizations stem from order comparisons of ranker positions.
Endless Loops: Detecting and Animating Periodic Patterns in Still Images
Venue: arXiv cs.CV 2021
Year: 2021

We present an algorithm for producing a seamless animated loop from a single image. The algorithm detects periodic structures, such as the windows of a building or the steps of a staircase, and generates a non-trivial displacement vector field that maps each segment of the structure onto a neighboring segment along a user- or auto-selected main direction of motion. This displacement field is used, together with suitable temporal and spatial smoothing, to warp the image and produce the frames of a continuous animation loop. Our cinemagraphs are created in under a second on a mobile device. Over 140,000 users downloaded our app and exported over 350,000 cinemagraphs. Moreover, we conducted two user studies that show that users prefer our method for creating surreal and structured cinemagraphs compared to more manual approaches and compared to previous methods.
Balancing Robustness and Sensitivity using Feature Contrastive Learning
Venue: arXiv cs.LG 2021
Year: 2021

It is generally believed that robust training of extremely large networks is critical to their success in real-world applications. However, when taken to the extreme, methods that promote robustness can hurt the model's sensitivity to rare or underrepresented patterns. In this paper, we discuss this trade-off between sensitivity and robustness to natural (non-adversarial) perturbations by introducing two notions: contextual feature utility and contextual feature sensitivity. We propose Feature Contrastive Learning (FCL) that encourages a model to be more sensitive to the features that have higher contextual utility. Empirical results demonstrate that models trained with FCL achieve a better balance of robustness and sensitivity, leading to improved generalization in the presence of noise on both vision and NLP datasets.
Computational Morphology with Neural Network Approaches
Venue: arXiv cs.CL 2021
Year: 2021

Neural network approaches have been applied to computational morphology with great success, improving the performance of most tasks by a large margin and providing new perspectives for modeling. This paper starts with a brief introduction to computational morphology, followed by a review of recent work on computational morphology with neural network approaches, to provide an overview of the area. In the end, we will analyze the advantages and problems of neural network approaches to computational morphology, and point out some directions to be explored by future research and study.
DistTune: Distributed Fine-Grained Adaptive Traffic Speed Prediction for Growing Transportation Networks
Venue: arXiv cs.LG 2021
Year: 2021

Over the past decade, many approaches have been introduced for traffic speed prediction. However, providing fine-grained, accurate, time-efficient, and adaptive traffic speed prediction for a growing transportation network where the size of the network keeps increasing and new traffic detectors are constantly deployed has not been well studied. To address this issue, this paper presents DistTune based on Long Short-Term Memory (LSTM) and the Nelder-Mead method. Whenever encountering an unprocessed detector, DistTune decides if it should customize an LSTM model for this detector by comparing the detector with other processed detectors in terms of the normalized traffic speed patterns they have observed. If similarity is found, DistTune directly shares an existing LSTM model with this detector to achieve time-efficient processing. Otherwise, DistTune customizes an LSTM model for the detector to achieve fine-grained prediction. To make DistTune even more time-efficient, DistTune performs on a cluster of computing nodes in parallel. To achieve adaptive traffic speed prediction, DistTune also provides LSTM re-customization for detectors that suffer from unsatisfactory prediction accuracy due to for instance traffic speed pattern change. Extensive experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistTune. The results demonstrate that DistTune provides fine-grained, accurate, time-efficient, and adaptive traffic speed prediction for a growing transportation network.
Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder
Venue: arXiv cs.LG 2021
Year: 2021

In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to predict risk in value-based care for incorporation into CMS Innovation Center payment and service delivery models. Recently, modern language models have played key roles in a number of health related tasks. This paper presents, to the best of our knowledge, the first application of these models to patient readmission prediction. To facilitate this, we create a dataset of 1.2 million medical history samples derived from the Limited Dataset (LDS) issued by CMS. Moreover, we propose a comprehensive modeling solution centered on a deep learning framework for this data. To demonstrate the framework, we train an attention-based Transformer to learn Medicare semantics in support of performing downstream prediction tasks thereby achieving 0.91 AUC and 0.91 recall on readmission classification. We also introduce a novel data pre-processing pipeline and discuss pertinent deployment considerations surrounding model explainability and bias.
L1 Regression with Lewis Weights Subsampling
Venue: arXiv cs.LG 2021
Year: 2021

We consider the problem of finding an approximate solution to $\ell_1$ regression while only observing a small number of labels. Given an $n \times d$ unlabeled data matrix $X$, we must choose a small set of $m \ll n$ rows to observe the labels of, then output an estimate $\widehatβ$ whose error on the original problem is within a $1 + \varepsilon$ factor of optimal. We show that sampling from $X$ according to its Lewis weights and outputting the empirical minimizer succeeds with probability $1-δ$ for $m > O(\frac{1}{\varepsilon^2} d \log \frac{d}{\varepsilon δ})$. This is analogous to the performance of sampling according to leverage scores for $\ell_2$ regression, but with exponentially better dependence on $δ$. We also give a corresponding lower bound of $Ω(\frac{d}{\varepsilon^2} + (d + \frac{1}{\varepsilon^2}) \log\frac{1}δ)$.
Anabranch Network for Camouflaged Object Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings. The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image. This problem has not been well studied in spite of a wide range of potential applications including the preservation of wild animals and the discovery of new species, surveillance systems, search-and-rescue missions in the event of natural disasters such as earthquakes, floods or hurricanes. This paper addresses a new challenging problem of camouflaged object segmentation. To address this problem, we provide a new image dataset of camouflaged objects for benchmarking purposes. In addition, we propose a general end-to-end network, called the Anabranch Network, that leverages both classification and segmentation tasks. Different from existing networks for segmentation, our proposed network possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image, which is then fused into the main branch for segmentation to boost up the segmentation accuracy. Extensive experiments conducted on the newly built dataset demonstrate the effectiveness of our network using various fully convolutional networks. \url{https://sites.google.com/view/ltnghia/research/camo}
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection
Venue: arXiv cs.LG 2021
Year: 2021

Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes performance over a possibly infinite random sequence of Markov Decision Processes (MDPs), each of which drawn from some unknown distribution. We call each such MDP a context. Most related works make strong assumptions such as knowledge about the distribution over contexts, the existence of pre-training phases, or a priori knowledge about the number, sequence, or boundaries between contexts. We introduce an algorithm that efficiently learns policies in non-stationary environments. It analyzes a possibly infinite stream of data and computes, in real-time, high-confidence change-point detection statistics that reflect whether novel, specialized policies need to be created and deployed to tackle novel contexts, or whether previously-optimized ones might be reused. We show that (i) this algorithm minimizes the delay until unforeseen changes to a context are detected, thereby allowing for rapid responses; and (ii) it bounds the rate of false alarm, which is important in order to minimize regret. Our method constructs a mixture model composed of a (possibly infinite) ensemble of probabilistic dynamics predictors that model the different modes of the distribution over underlying latent MDPs. We evaluate our algorithm on high-dimensional continuous reinforcement learning problems and show that it outperforms state-of-the-art (model-free and model-based) RL algorithms, as well as state-of-the-art meta-learning methods specially designed to deal with non-stationarity.
DeepStrike: Remotely-Guided Fault Injection Attacks on DNN Accelerator in Cloud-FPGA
Venue: arXiv cs.CR 2021
Year: 2021

As Field-programmable gate arrays (FPGAs) are widely adopted in clouds to accelerate Deep Neural Networks (DNN), such virtualization environments have posed many new security issues. This work investigates the integrity of DNN FPGA accelerators in clouds. It proposes DeepStrike, a remotely-guided attack based on power glitching fault injections targeting DNN execution. We characterize the vulnerabilities of different DNN layers against fault injections on FPGAs and leverage time-to-digital converter (TDC) sensors to precisely control the timing of fault injections. Experimental results show that our proposed attack can successfully disrupt the FPGA DSP kernel and misclassify the target victim DNN application.
Deep learning for solution and inversion of structural mechanics and vibrations
Venue: arXiv cs.LG 2021
Year: 2021

Deep learning has been the most popular machine learning method in the last few years. In this chapter, we present the application of deep learning and physics-informed neural networks concerning structural mechanics and vibration problems. Demonstration problems involve de-noising data, solution to time-dependent ordinary and partial differential equations, and characterizing the system's response for a given data.
Generalizing Non-Punctuality for Timed Temporal Logic with Freeze Quantifiers
Venue: arXiv cs.LO 2021
Year: 2021

Metric Temporal Logic (MTL) and Timed Propositional Temporal Logic (TPTL) are prominent real-time extensions of Linear Temporal Logic (LTL). In general, the satisfiability checking problem for these extensions is undecidable when both the future U and the past S modalities are used. In a classical result, the satisfiability checking for MITL[U,S], a non punctual fragment of MTL[U,S], is shown to be decidable with EXPSPACE complete complexity. Given that this notion of non punctuality does not recover decidability in the case of TPTL[U,S], we propose a generalization of non punctuality called \emph{non adjacency} for TPTL[U,S], and focus on its 1-variable fragment, 1-TPTL[U,S]. While non adjacent 1-TPTL[U,S] appears to be be a very small fragment, it is strictly more expressive than MITL. As our main result, we show that the satisfiability checking problem for non adjacent 1-TPTL[U,S] is decidable with EXPSPACE complete complexity.
Micro Analysis of Natural Forking in Blockchain Based on Large Deviation Theory
Venue: arXiv cs.CR 2021
Year: 2021

Natural forking in blockchain refers to a phenomenon that there are a set of blocks at one block height at the same time, implying that various nodes have different perspectives of the main chain. Natural forking might give rise to multiple adverse impacts on blockchain, jeopardizing the performance and security of the system consequently. However, the ongoing literature in analyzing natural forking is mainly from the macro point of view, which is not sufficient to incisively understand this phenomenon. In this paper, we fill this gap through leveraging the large deviation theory to conduct a microscopic study of natural forking, which resorts to investigating the instantaneous difference between block generation and dissemination in blockchain. Our work is derived comprehensively and complementarily via a three-step process, where both the natural forking probability and its decay rate are presented. Through solid theoretical derivation and extensive numerical simulations, we find 1) the probability of the mismatch between block generation and dissemination exceeding a given threshold dwindles exponentially with the increase of natural forking robustness related parameter or the difference between the block dissemination rate and block creation rate; 2) the natural forking robustness related parameter may emphasize a more dominant effect on accelerating the abortion of natural forking in some cases; 3) when the self-correlated block generation rate is depicted as the stationary autoregressive process with a scaling parameter, it is found that setting a lower scaling parameter may speed up the failure of natural forking. These findings are valuable since they offer a fresh theoretical basis to engineer optimal countermeasures for thwarting natural forking and thereby enlivening the blockchain network.
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data. However, its evaluation has long been a problem: previous works either took costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled data -- which, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset. This problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE. To evaluate DS-RE models in a more credible way, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. The experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared to previous methods. We hope that both our manual test sets and novel observations can help advance future DS-RE research.